Elastic Net Regression
Elastic Net Regression is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Health Sciences sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.
🎯 What is it for?
Elastic Net Regression automatically performs all assumption checks required for the data type (normality, homogeneity of variance, etc.) in the background and presents the results in a clear table + chart. Automatic APA 7-formatted interpretation, effect sizes (Cohen’s d, η², R²) and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Health Sciences domain
- To produce APA 7-compatible result tables for academic publications
- Hypothesis testing and decision-making processes
- Undergraduate / master’s / PhD theses after the appropriate method has been selected
📐 Assumptions
- Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
- Independent observations — Observations must come from individuals independent of one another
- Sufficient sample size — The minimum n requirement for the analysis must be met
- Outlier check — Outliers must be detected and evaluated
If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.
🛠 How to do it in MerQur
Load the data. Select the sample file from File → Open. MerQur auto-detects column types.
Select the analysis. From the left side select Elastic Net Regression.
Panel assignments (form fields in the program):
- Target Column:
risk_skor - Prediktorler:
[x01, x02, x03, ... +37 adet] - Yontem:
elasticnet - Alpha:
0.5 - L1 orani:
0.5 - CV kati:
5 - standardize:
True
Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).
▶ Run — click the button. Results are produced automatically as a table + chart.
📄 Export to Word. APA 7-formatted report with italic statistical symbols.
📊 Sample Dataset — Health Sciences
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Health Sciences:
Tip/57_elasticnet_risk_score.xlsx
🎬 Scenario
We model a risk score from 40 predictors with Elastic Net. For many clustered predictors, Elastic Net is appropriate.
⚙️ Variable Selection
- Dependent variable: risk_score
- Predictor(s): x01..40
Data Preview (First 5 Rows)
| patient_id | x01 | x02 | x03 | x04 | x05 | x06 | x07 | x08 | x09 | x10 | x11 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0 | 0.409 | -0.849 | 0.533 | -0.857 | 0.338 | -0.198 | 0.541 | -0.277 | -0.902 | -0.201 | -0.191 |
| 2.0 | -0.535 | -0.366 | -1.263 | -0.893 | -0.548 | -0.963 | -0.709 | -0.685 | -0.669 | -0.271 | -0.505 |
| 3.0 | 0.372 | 0.648 | -0.12 | 0.018 | 0.298 | 0.479 | 0.702 | -0.041 | 1.058 | 1.103 | 0.785 |
| 4.0 | 0.208 | 1.04 | 0.535 | 0.609 | 0.038 | 0.982 | 0.286 | 1.463 | 1.0 | 1.561 | 0.848 |
| 5.0 | -0.721 | -1.367 | -0.814 | -0.296 | -1.074 | -0.804 | -1.014 | -0.865 | 0.183 | -0.513 | -0.156 |
n = 220 · Columns (first 12 columns): patient_id, x01, x02, x03, x04, x05, x06, x07, x08, x09, x10, x11
📈 MerQur Output
💬 Interpretation
We modeled a risk score from 40 predictors with Elastic Net. Elastic Net blends the ridge (L2) and lasso (L1) penalties (L1 ratio = 0.5): it both keeps groups of correlated variables together (ridge property) and zeroes out redundant ones (lasso property). It thus provides a balanced model with high-dimensional, clustered predictors. In medicine it is chosen when there are many clustered biomarkers/features, where lasso or ridge alone is insufficient.
⚠ Common Mistakes
- Misidentifying the data type (e.g., loading a categorical variable as numeric)
- Skipping assumption checks and going straight to the p-value
- Failing to report effect size — APA 7 requires both p and effect size
- Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
- Not switching to a non-parametric alternative when n is insufficient
📹 Video Walkthrough
Watch the video below for an end-to-end walkthrough of this analysis on a Health Sciences file.
▶ Elastic Net Regression — video walkthrough
A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic
📚 If You Used This Analysis, Cite MerQur
If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
For BibTeX, RIS, EndNote and the English citation form: all citation formats →
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.